• Journal of the Korean Society of Safety
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• v.34 no.6
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• pp.29-37
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• 2019

Since construction sites are exposed to outdoor environments, working conditions are significantly dangerous. Thus, wearing of the personal protective equipments such as safety helmet is very important for worker safety. However, construction workers are often wearing-off the helmet as inconvenient and uncomportable. As a result, a small mistake may lead to serious accident. For this, checking of wearing safety helmet is important task to safety managers in field. However, due to the limited time and manpower, the checking can not be executed for every individual worker spread over a large construction site. Therefore, if an automatic checking system is provided, field safety management should be performed more effectively and efficiently. In this study, applicability of deep learning based computer vision technology is investigated for automatic checking of wearing safety helmet in construction sites. Faster R-CNN deep learning algorithm for object detection and classification is employed to develop the automatic checking model. Digital camera images captured in real construction site are used to validate the proposed model. Based on the results, it is concluded that the proposed model may effectively be used for automatic checking of wearing safety helmet in construction site.

• Journal of Drive and Control
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• v.17 no.1
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• pp.37-43
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• 2020

An intelligent excavator can be divided into Machine Guidance (MG), semi-automatic, and unmanned by technology. The MG technology excavator is equipped with a tilt sensor on each link of the excavator and a GPS is installed on the excavator body to inform the user of the position of the excavator bucket end. Machine control (MC) technology that assists the user's work can be divided into semi-automatic and fully automatic technology. The semi-automatic MC equipment has already been commercialized by Komatsu and Caterpillar. The MC excavator is equipped with an electro-hydraulic system, sensors and controllers to control the excavator bucket end according to the user's needs. In this study, the semi-automated excavator modified based on manual excavator, is equipped with an electro-hydraulic system, a controller system, multi-sensors and a control algorithm is developed to assist in excavation work such as leveling and grading. By applying the developed technology, it was possible to confirm productivity improvement compared to manual digging and leveling work. In the future, further research to improve the accuracy of the hydraulic precision control and collaborative work with heterogeneous construction equipment such as dump truck and automated collaboration tasks technology could be developed.

• Journal of the Korea Safety Management & Science
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• v.26 no.2
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• pp.43-53
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• 2024

This study analyzes the importance-performance analysis (IPA) of the 10 dimensions of the smart construction safety management system, and analyzes which dimensions are important and which dimensions are performing to determine key improvement tasks, incremental improvement tasks, Maintenance and reinforcement tasks and continuous maintenance tasks were derived. Among the 10 dimensions of the smart construction safety management system, the dimensions that are recognized as important by all field managers and field workers and have high performance are the automatic risk displacement measurement system, smart environmental sensor system, and heavy equipment seizure prevention system. However, areas that were perceived as having high importance but low performance were worker location tracking systems, smart safety helmet chin muscles, and smart safety ring fastening. Among the smart construction safety management systems perceived by field managers, areas for key improvement with high importance and low performance included worker location tracking system and smart safety ring fastening. Among the smart construction safety management systems perceived by field workers, the area for key improvement with high importance and low performance was the automatic risk displacement measurement system.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.721-727
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• 2022

Construction safety remains an ongoing concern, and project managers have been increasingly forced to cope with myriad uncertainties related to human operations on construction sites and the lack of a skilled workforce in hazardous circumstances. Various construction fatality monitoring systems have been widely proposed as alternatives to overcome these difficulties and to improve safety management performance. In this study, we propose an intelligent, automatic control system that can proactively protect workers using both the analysis of big data of past safety accidents, as well as the real-time detection of worker non-compliance in using personal protective equipment (PPE) on a construction site. These data are obtained using computer vision technology and data analytics, which are integrated and reinforced by lessons learned from the analysis of big data of safety accidents that occurred in the last 10 years. The system offers data-informed recommendations for high-risk workers, and proactively eliminates the possibility of safety accidents. As an illustrative case, we selected a pilot project and applied the proposed system to workers in uncontrolled environments. Decreases in workers PPE non-compliance rates, improvements in variable compliance rates, reductions in severe fatalities through guidelines that are customized according to the worker, and accelerations in safety performance achievements are expected.

• Journal of the Korea Institute of Building Construction
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• v.8 no.1
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• pp.63-70
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• 2008

Management process of apartment buildings construction has increased because the after service of construction company meet the needs of customers. Many defect data, which was inspected by construction company or customers before moving into a new apartment house, were classified by field engineers and then communicated to corresponding subcontractors. The classification process needs to be performed by an expert engineer because there is so much data, it is unfeasible to complete in a short period of time. For this classification process, an automatic classification system using case base reasoning (CBR) should be considered. This research proposed a defect management system with automatic classification system using CBR. This constructed defect management system consists of cyber after service system for tenants and the whole defect management process of construction, preservation and management of apartment buildings. This system could improve the efficiency of expert work in terms of time and accuracy, as well as helping laymen users to conduct defect classification work as experts do.

• The KSFM Journal of Fluid Machinery
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• v.17 no.2
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• pp.54-58
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• 2014

The method of residential waste and food waste collecting is changed into automatic waste collection(AWC) system from direct collection by human resource. To solve the problem caused by odour from AWC facilities, the structure of input facilities, conveying pipes and collecting facilities have been changed into closed and sealed construction to enclosed the facilities and collect odour efficiently. Based on cases and experiences, to treat variable odour matters which are caused by food waste, chemical cleaning method is preferable method to cope with odour caused by food waste.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.470-475
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• 2011

The objective of this study is the developement health & disaster monitoring measurement using ubiquitous active communication digital datalogger system for monitoring measurement of railway construction sites. For the replacement of current passive data communication, ubiquitous active communication digital datalogger system is studied for the first time with in a country. Therefore data communication method and analyzing program of automatic measurement data is developed for the global positioning automatic digital datalogger system. The results of this study will be using both real time automatic monitoring measurement and health & disaster monitoring measurement of railway structures.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2002-2007
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• 2010

The objective of this study is the developement monitoring measurement using global positioning digital datalogger system for monitoring measurement of railway construction sites. For the replacement of current passive data communication, Global positioning digital datalogger system using active communication is studied for the first time with in a country. Therefore data communication method and analyzing program of automatic measurement data is developed for the global positioning automatic digital datalogger system. The results of this study will be using real time automatic monitoring measurement of railway structures.

• Journal of the Korea Institute of Building Construction
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• v.2 no.1
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• pp.80-87
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• 2002

• Journal of the Korea Institute of Building Construction
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• v.2 no.4
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• pp.50-54
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• 2002